ISTINYE UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| Data Science (Master) (with Thesis) (English) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
Course Introduction and Application Information
| Course Code: | DATS5101 | ||||
| Course Name: | Database Applications | ||||
| Semester: | Fall | ||||
| Course Credits: |
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| Language of instruction: | English | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Compulsory Courses | ||||
| Course Level: |
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| Mode of Delivery: | Face to face | ||||
| Course Coordinator: | Doç. Dr. ŞEBNEM ÖZDEMİR | ||||
| Course Lecturer(s): |
Prof. Dr. METİN ZONTUL |
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| Course Assistants: |
Course Objective and Content
| Course Objectives: | This course is designed to provide students with a comprehensive understanding of database concepts, practical skills in database design and implementation, and the integration of databases within application development. Students will learn the theoretical foundations of database systems, gain hands-on experience with both SQL and NoSQL databases, and leverage this knowledge to build efficient, reliable, and scalable database applications. Through course assignments and projects, students will demonstrate their ability to not only construct and manage databases but also integrate them seamlessly into real-world applications. |
| Course Content: | 1. Overview of database systems, architectures, models, and components. 2. Principles and methodologies for database design and data modeling. 3. Evaluation and utilization of SQL for database manipulation and querying. 4. Introduction to NoSQL databases and their applications in modern data environments. 5. Implementing and managing database solutions in various application contexts. |
Learning Outcomes
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The students who have succeeded in this course;
1) Demonstrate a solid understanding of database systems, models, and architecture. 2) Design efficient database schemas based on data modeling methodologies. 3) Employ SQL effectively for data manipulation, storage, and retrieval from relational databases. 4) Recognize the use cases for NoSQL databases and apply them to appropriate scenarios. 5) Integrate database systems effectively within software and web applications. |
Course Flow Plan
| Week | Subject | Related Preparation |
| 1) | Introduction to databases and their role in application development. | |
| 2) | Database system architectures and core components. | |
| 3) | Data models and database design methodologies. | |
| 4) | Introduction to SQL and basic data manipulation. | |
| 5) | Advanced SQL techniques for complex querying. | |
| 6) | Database normalization and schema refinement. | |
| 7) | Transaction management and concurrency control. | |
| 8) | Midterm Exam - Covering weeks 1-7 material. | |
| 9) | Introduction to NoSQL databases and data storage alternatives. | |
| 10) | NoSQL database design and document-based databases. | |
| 11) | Querying NoSQL databases and data aggregation frameworks. | |
| 12) | Building applications with SQL databases. | |
| 13) | Building applications with NoSQL databases. | |
| 14) | Security in database applications and best practices. | |
| 15) | Performance tuning and optimization of database applications. | |
| 16) | Final Exam - Comprehensive analysis of course content. |
Sources
| Course Notes / Textbooks: | Herhangi bir ders kitabı bulunmamaktadır. There is no textbook. |
| References: | Güncel makaleler, kitaplar kullanılacaktır. Current articles and books will be used. |
Course - Program Learning Outcome Relationship
| Course Learning Outcomes | 1 |
2 |
3 |
4 |
5 |
|---|---|---|---|---|---|
| Program Outcomes | |||||
| 1) Students who successfully complete this program, Knows the scope of technical applications of data science and the tools that can be used. | 3 | 3 | 3 | 3 | 3 |
| 2) Students who successfully complete this program, Knows the effects of application results on society-culture-law. | 3 | 3 | 3 | 3 | 3 |
| 3) Students who complete this program; Recognize the mathematics and code in application processes | 3 | 3 | 3 | 3 | 3 |
| 4) Students who complete this program; Explain the effects of the processes in data science on the output and the individual | 3 | 3 | 3 | 3 | 3 |
| 5) Students who successfully complete this program, Understands the insight-foresight and foresight created by data science as a whole in the face of a certain discipline/case. | 3 | 3 | 3 | 3 | 3 |
Course - Learning Outcome Relationship
| No Effect | 1 Lowest | 2 Average | 3 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Students who successfully complete this program, Knows the scope of technical applications of data science and the tools that can be used. | 3 |
| 2) | Students who successfully complete this program, Knows the effects of application results on society-culture-law. | 3 |
| 3) | Students who complete this program; Recognize the mathematics and code in application processes | 3 |
| 4) | Students who complete this program; Explain the effects of the processes in data science on the output and the individual | 3 |
| 5) | Students who successfully complete this program, Understands the insight-foresight and foresight created by data science as a whole in the face of a certain discipline/case. | 3 |
Assessment & Grading
| Semester Requirements | Number of Activities | Level of Contribution |
| Midterms | 1 | % 40 |
| Final | 1 | % 60 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 40 | |
| PERCENTAGE OF FINAL WORK | % 60 | |
| total | % 100 | |
Workload and ECTS Credit Calculation
| Activities | Number of Activities | Preparation for the Activity | Spent for the Activity Itself | Completing the Activity Requirements | Workload | ||
| Course Hours | 14 | 0 | 3 | 42 | |||
| Midterms | 1 | 80 | 1 | 81 | |||
| Final | 1 | 100 | 1 | 101 | |||
| Total Workload | 224 | ||||||